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In this article, we suggest that the study of social interactions and the development of a "sense of agency" in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them "explainable." The introduction of automated systems, and more broadly of Artificial Intelligence (AI), into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences - an experience that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals' sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human-AI interactions in the light of human-human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human-AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents.

Repeated interactions with automated systems are known to affect how agents experience their own actions and choices. The present study explores the possibility of partially restoring sense of agency in operators interacting with automated systems by providing additional information about the system's decision, i.e. its confidence. To do so, we implemented an obstacle avoidance task with different levels of automation and explicability. Levels of automation were varied by implementing conditions in which the participant was free or not free to choose which direction to take, whereas levels of explicability were varied by providing or not providing the participant with the system's confidence in the direction to take. We first assessed how automation and explicability interacted with participants' sense of agency, and then tested whether increased self-agency was systematically associated with greater confidence in the decision and improved system acceptability. The results showed an overall positive effect of system assistance. Providing additional information about the system's decision (explicability effect) and reducing the cognitive load associated with the decision itself (automation effect) was associated with stronger sense of agency, greater confidence in the decision, and better performance. In addition to the positive effects of system assistance, acceptability scores revealed that participants perceived "explicable" systems more favorably. These results highlight the potential value of studying self-agency in human-machine interaction as a guideline for making automation technologies more acceptable and, ultimately, improving the usefulness of these technologies.